Abstract
This contribution extends generalized LVQ, generalized relevance LVQ, and robust soft LVQ to the graph domain. The proposed approaches are based on the basic learning graph quantization (lgq) algorithm using the orbifold framework. Experiments on three data sets show that the proposed approaches outperform lgq and lgq2.1.
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References
Borzellino, J.E.: Riemannian geometry of orbifolds, PhD thesis, University of California, Los Angelos (1992)
Cour, T., Srinivasan, P., Shi, J.: Balanced graph matching. In: NIPS (2006)
Gold, S., Rangarajan, A.: Graduated Assignment Algorithm for Graph Matching. IEEE Transactions on PAMI 18, 377–388 (1996)
Hammer, B., Villmann, T.: Generalized relevance learning vector quantization. Neural Network 15, 1059–1068 (2002)
Hammer, B., Strickert, M., Villmann, T.: Supervised neural gas with general similarity measure. Neural Processing Letters 21(1), 21–44 (2005)
Jain, B., Obermayer, K.: Structure Spaces. Journal of Machine Learning Research 10, 2667–2714 (2009)
Jain, B.J., Obermayer, K.: Algorithms for the Sample Mean of Graphs. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 351–359. Springer, Heidelberg (2009)
Jain, B.J., Srinivasan, S.D., Tissen, A., Obermayer, K.: Learning graph quantization. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds.) SSPR&SPR 2010. LNCS, vol. 6218, pp. 109–118. Springer, Heidelberg (2010)
Jain, B., Obermayer, K.: Extending Bron Kerbosch for Solving the Maximum Weight Clique Problem. arXiv:1101.1266v1 (2011)
Jain, B., Obermayer, K.: Maximum Likelihood for Gaussians on Graphs. In: Jiang, X., Ferrer, M., Torsello, A. (eds.) GbRPR 2011. LNCS, vol. 6658, pp. 62–71. Springer, Heidelberg (2011)
Kohonen, T.: Self-organizing maps. Springer, Heidelberg (1997)
Kohonen, T., Somervuo, P.: Self-organizing maps of symbol strings. Neurocomputing 21(1-3), 19–30 (1998)
Norkin, V.I.: Stochastic generalized-differentiable functions in the problem of nonconvex nonsmooth stochastic optimization. Cybernetics 22(6), 804–809 (1986)
Riesen, K., Bunke, H.: IAM graph database repository for graph based pattern recognition and machine learning. In: da Vitoria Lobo, N., Kasparis, T., Roli, F., Kwok, J.T., Georgiopoulos, M., Anagnostopoulos, G.C., Loog, M. (eds.) S+SSPR 2008. LNCS, vol. 5342, pp. 287–297. Springer, Heidelberg (2008)
Riesen, K., Bunke, H.: Graph Classification by Means of Lipschitz Embedding. IEEE Transactions on Systems, Man, and Cybernetics 39(6), 1472–1483 (2009)
Sato, A., Yamada, K.: Generalized learning vector quantization. In: NIPS (1996)
Seo, S., Obermayer, K.: Soft learning vector quantization. Neural Computation 15(7), 1589–1604 (2003)
Sumervuo, P., Kohonen, T.: Self-organizing maps and learning vector quantization for feature sequences. Neural Processing Letters 10(2), 151–159 (1999)
Umeyama, S.: An eigendecomposition approach to weighted graph matching problems. IEEE Transactions on PAMI 10(5), 695–703 (1988)
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Jain, B.J., Obermayer, K. (2011). Generalized Learning Graph Quantization. In: Jiang, X., Ferrer, M., Torsello, A. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2011. Lecture Notes in Computer Science, vol 6658. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20844-7_13
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DOI: https://doi.org/10.1007/978-3-642-20844-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20843-0
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